lakshay-arora

# importing pandas to read the CSV file
import pandas as pd
# read the data
data_classification = pd.read_csv('datasets/loan_train_data.csv')
# view the top rows of the data
data_classification.head()

lakshay-arora

# start flask
app = Flask(__name__)

# render default webpage
@app.route('/')
def home():
    return render_template('home.html')

# when the post method detect, then redirect to success function
@app.route('/', methods=['POST', 'GET'])

lakshay-arora

# importing the required libraries
from flask import Flask, render_template, request, redirect, url_for
from joblib import load
from get_tweets import get_related_tweets

# load the pipeline object
pipeline = load("text_classification.joblib")

# function to get results for a particular text query
def requestResults(name):

lakshay-arora

def get_related_tweets(text_query):
    # list to store tweets
    tweets_list = []
    # no of tweets
    count = 50
    try:
        # Pulling individual tweets from query
        for tweet in api.search(q=text_query, count=count):
            print(tweet.text)
            # Adding to list that contains all tweets

lakshay-arora

# import required libraries
import tweepy
import time
import pandas as pd
pd.set_option('display.max_colwidth', 1000)

# api key
api_key = "Enter API Key Here"
# api secret key
api_secret_key = "Enter API Secret Key Here."

lakshay-arora

# import joblib
from joblib import load

# sample tweet text
text = ["Virat Kohli, AB de Villiers set to auction their 'Green Day' kits from 2016 IPL match to raise funds"]

# load the saved pipleine model
pipeline = load("text_classification.joblib")

# predict on the sample tweet text

lakshay-arora

# import joblib
from joblib import dump

# dump the pipeline model
dump(pipeline, filename="text_classification.joblib")

lakshay-arora

# sample tweet
text = ["Virat Kohli, AB de Villiers set to auction their 'Green Day' kits from 2016 IPL match to raise funds"]

# predict the label using the pipeline
pipeline.predict(text)
## >> array([0])

lakshay-arora

# define the stages of the pipeline
pipeline = Pipeline(steps= [('tfidf', TfidfVectorizer(lowercase=True,
                                                      max_features=1000,
                                                      stop_words= ENGLISH_STOP_WORDS)),
                            ('model', LogisticRegression())])

# fit the pipeline model with the training data                            
pipeline.fit(train.tweet, train.label)

lakshay-arora

# create the object of LinearRegression Model
model_LR = LogisticRegression()

# fit the model with the training data
model_LR.fit(train_idf, train.label)

# predict the label on the traning data
predict_train = model_LR.predict(train_idf)

# predict the model on the test data